Abstract 1057: Development/validation of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for cancer patients
Authors: Yuliang Sun, Tobias Meissner, Rachel Elsey, Leah Theisen, Crystal Hattum, Bing Xu, John Lee, Casey Williams
Journal: Cancer Research (2023)

Abstract Background Tumor complexity and heterogeneity require matching of actionable genomic alterations with available therapy to increase response rates and prolong survival in patients. However, it has been a long-standing challenge for treating oncologists to select an effective patient-specific therapeutic strategy due to the molecular rationale, disease relevance, and patient-specific issues. To provide a decision-support tool to aid oncologists and educate patients, we have developed a set of molecular and clinical evidence-based criteria as an algorithm for ranking therapeutic strategies in order to deliver optimal care and improve outcomes in patients with malignancy. Methods History of present illness (HPI) and comprehensive genomic profiling (CGP) results of 203 patients with malignant tumors were reviewed by our multidisciplinary Molecular Tumor Board (MTB) from June 2021 to August 2022, and therapeutic recommendations were provided with Matching Score based on molecular matching only as well as with Ranking Score calculated by our molecular and clinical evidence-based algorithm with the criteria not only focusing on molecular matching, but also including disease relevance, patient-specific clinical considerations and treatment availability as weighting factors (Cohort 2). The other 50 patients with previously treated solid tumors reviewed by MTB (before 2018) and treatment recommendations provided with Matching Score only, were used as a control group (Cohort 1). The matching rates from recommendations and treatment outcomes of the patients were then assessed. Results In Cohort 1, of the 50 patients, 33 patients (66%) received matched therapeutic plans recommended by our MTB. The other 17 patients were not on a matched plan from the MTB. Twelve patients (36.4% of 33 patients) achieved progression-free survival (PFS) at 12-week time point, including 8 patients (24.2%) achieved PFS at 6-month time point. The other 21 patients (63.6% of 33 patients) could not be assessed due to treatment termination per drug toxicity or death before the follow-up time point. The median PFS/overall survival (OS) for patients with a Matching Score &amp;gt; 50% (N = 17) were 7.5/10.5 months, whereas with Matching Score ≤ 50% (N = 16) were 3/7.35 months. In Cohort2, of the 203 patients, 89 (43.8%) patients have initiated matched therapeutic plans and follow-up is ongoing. Updated results will be presented at the meeting. Conclusion Our novel molecular and clinical evidence-based algorithm may be used to support oncologists decision-making to utilize the most clinically appropriate and effective therapeutic options. Further validation studies and development of an user-friendly computational ranking platform based on the algorithm are planned in order. Citation Format: Yuliang Sun, Tobias Meissner, Rachel Elsey, Leah Theisen, Crystal Hattum, Bing Xu, John Lee, Casey Williams. Development/validation of a molecular and clinical evidence-based algorithm for selecting optimal precision therapeutic strategy for cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1057.